A hybrid mammalian cell cycle model

TL;DR

This paper introduces a hybrid model of the mammalian cell cycle that simplifies complex biochemical dynamics by combining continuous and discrete elements, facilitating analysis of cell cycle regulation.

Contribution

It presents a novel hybridization approach to convert a smooth biochemical cell cycle model into a piecewise-smooth hybrid model using learning-based parameter estimation.

Findings

Hybrid model captures key cell cycle dynamics

Simplified reaction rates improve computational efficiency

Learning strategies effectively estimate hybrid model parameters

Abstract

Hybrid modeling provides an effective solution to cope with multiple time scales dynamics in systems biology. Among the applications of this method, one of the most important is the cell cycle regulation. The machinery of the cell cycle, leading to cell division and proliferation, combines slow growth, spatio-temporal re-organisation of the cell, and rapid changes of regulatory proteins concentrations induced by post-translational modifications. The advancement through the cell cycle comprises a well defined sequence of stages, separated by checkpoint transitions. The combination of continuous and discrete changes justifies hybrid modelling approaches to cell cycle dynamics. We present a piecewise-smooth version of a mammalian cell cycle model, obtained by hybridization from a smooth biochemical model. The approximate hybridization scheme, leading to simplified reaction rates and binary event location functions, is based on learning from a training set of trajectories of the smooth model. We discuss several learning strategies for the parameters of the hybrid model.